Some patients respond well to antidepressants, whereas others do not. The biological basis for differences among patients in antidepressant response is poorly understood. Pharmacogenetics seeks DNA markers that can predict medication treatment outcomes. We have DNA and clinical data from a prospective, double-blind, randomized pharmacogenetic study of 246 cognitively intact patients 65 years of age and older with major depression who were treated with either mirtazapine or paroxetine for 24 weeks. This dataset is particularly valuable for pharmacogenetics because of the contrasting actions of paroxetine and mirtazapine, and because older patients are highly vulnerable to medication adverse events. Analysis thus far has revealed significant predictors in genes encoding serotonergic and adrenergic receptors and reuptake transporters. We now propose to use this unique DNA and clinical resource to identify additional novel genetic markers for antidepressant efficacy and side effects. We will focus on two interacting systems, neurotrophins and the HPA axis. Neurotrophins, their receptors, and related signal transduction effectors are important in the therapeutic effects of antidepressants. We will target the BDNF system, which is strongly modulated by antidepressants. HPA axis dysregulation is likely to be key in the etiology of depression, and normalizing HPA axis function has therapeutic value. Genes involved in the regulation and actions of corticosteroids, which may exacerbate depression and are downregulated by mirtazapine, will be examined. Resequencing of coding and regulatory regions will be performed for selected genes in a discovery sample of 24 subjects. SNPs identified by resequencing will be chosen for further study based bioinformatic assessment of their potential functional significance, and genotyping will be performed in all 246 samples. Inferred haplotypes as well as unphased diplotypes will be used as predictors, with appropriate controls for multiple testing. In addition, an indirect candidate gene approach will be utilized based on linkage disequilibrium data from validated SNPs in the Perlegen database. The primary outcome measures will be change in mood (HDRS-17, CDS), the severity of adverse events, the frequency of treatment discontinuations, and cognitive changes. Key covariates such as compliance, dosing, and plasma drug levels will be included in the analyses. Genomic controls will also be tested. This work will provide a targeted pharmacogenetic analysis in geriatric patients of two interrelated biological systems emerging as important in antidepressant therapeutics. Major depression is a prevalent psychiatric disorder among the elderly. Many patients are resistant to antidepressant medication treatment. A method for identifying patients likely to benefit from antidepressant treatment would decrease patient distress and save health care dollars. [unreadable] [unreadable] [unreadable]